Computer Science > Machine Learning
[Submitted on 1 Jun 2022 (v1), last revised 10 Oct 2022 (this version, v2)]
Title:In the Eye of the Beholder: Robust Prediction with Causal User Modeling
View PDFAbstract:Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content -- making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.
Submission history
From: Amir Feder [view email][v1] Wed, 1 Jun 2022 11:33:57 UTC (1,593 KB)
[v2] Mon, 10 Oct 2022 16:55:36 UTC (1,666 KB)
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